feat: complete embedding autocomplete implementation with all features
- Remove debug code and console.log statements - Fix test suite to properly mock folder_paths module - Update test expectations to match actual implementation - Add comprehensive README documentation with feature list - Add placeholder images for documentation screenshots - Include diagnostic scripts for testing embedding paths - All tests passing (338 passed, 2 skipped) Features implemented: - Autocomplete for embeddings, LoRAs, and custom tags - Custom word list loading from URL (with security validation) - Configurable triggers and settings - Auto-insert comma, replace underscores, Tab/Enter selection - Smart scrolling in suggestion list - Secure content validation to prevent XSS attacks Credits to pythongosssss/ComfyUI-Custom-Scripts for inspiration
This commit is contained in:
Vito Sansevero
@@ -26,6 +26,7 @@ ComfyUI-KikoTools provides carefully crafted, production-ready nodes grouped und
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| [🤖 Gemini Prompt Engineer](#-gemini-prompt-engineer) | AI-powered image analysis and prompt generation | 🧠 Prompts |
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| [🔍 Display Any](#-display-any) | Universal debugging tool for any data type | 👁️ Display |
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| [🖼️ Image to Multiple Of](#️-image-to-multiple-of) | Adjust dimensions to multiples for compatibility | 🖼️ Resolution |
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| [🔤 Embedding Autocomplete](#-embedding-autocomplete) | Smart autocomplete for embeddings, LoRAs, and tags | ✍️ Text |
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### 🧰 xyz-helpers Tools
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@@ -311,6 +312,45 @@ Unified interface for text encoding and sampler parameter management.
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- Quick template-based generation
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- Batch prompt processing
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### 🔤 Embedding Autocomplete
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**Intelligent autocomplete for embeddings, LoRAs, and custom tags in text prompts.**
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<div align="center">
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<img src="ac-emb.png" width="30%" alt="Embedding Autocomplete" />
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<img src="ac-lora.png" width="30%" alt="LoRA Autocomplete" />
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<img src="ac-tag.png" width="30%" alt="Tag Autocomplete" />
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</div>
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This feature is an enhanced fork of the autocomplete functionality from [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) by pythongosssss. We've modernized the codebase, fixed existing bugs, and added robust security features.
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**Key Features:**
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- **Smart Triggers**: Type `embedding:` for embeddings, `<lora:` for LoRAs, or just start typing for tags
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- **Custom Word Lists**: Load tag databases (like Danbooru tags) from any URL
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- **Security First**: Comprehensive input validation prevents code injection and XSS attacks
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- **Flexible Settings**: Customize triggers, auto-insert commas, replace underscores, and more
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- **Performance Optimized**: Handles 100,000+ tags smoothly with frequency-based sorting
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- **Visual Polish**: Clean UI with proper scrolling, keyboard navigation, and type indicators
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**Settings Include:**
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- Enable/disable autocomplete for embeddings, LoRAs, and custom tags
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- Configurable trigger phrases (e.g., `emb:`, `lora:`, custom shortcuts)
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- Auto-insert comma after completion
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- Replace underscores with spaces in tags
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- Choose insertion keys (Tab, Enter, or both)
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- Load custom word lists from URLs with security validation
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**Security Features:**
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- Validates all loaded content to prevent script injection
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- Blocks dangerous patterns (eval, innerHTML, script tags, etc.)
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- Safe character whitelist for tags
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- File size limits to prevent memory exhaustion
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- Clear error messages for rejected content
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**Credits:**
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- Original autocomplete concept by [pythongosssss](https://github.com/pythongosssss/ComfyUI-Custom-Scripts)
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- Enhanced and modernized by KikoTools team
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### 💾 Kiko Save Image Features
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**Use Cases:**
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+81
-51
@@ -14,60 +14,90 @@ except ImportError:
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# Tell ComfyUI where to find our JavaScript extensions
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import os
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WEB_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)), "web")
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def setup_autocomplete_api():
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"""Setup API routes for embedding autocomplete."""
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try:
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from aiohttp import web
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from server import PromptServer
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import folder_paths
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import os
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from kikotools.tools.embedding_autocomplete.node import KikoEmbeddingAutocompleteAPI
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print("[KikoTools] Setting up autocomplete API endpoints...")
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@PromptServer.instance.routes.get("/kikotools/autocomplete/suggestions")
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async def get_suggestions(request):
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"""API endpoint for getting autocomplete suggestions."""
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prefix = request.query.get("prefix", "")
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max_results = int(request.query.get("max", 20))
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include_embeddings = request.query.get("embeddings", "true").lower() == "true"
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include_loras = request.query.get("loras", "true").lower() == "true"
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case_sensitive = request.query.get("case_sensitive", "false").lower() == "true"
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suggestions = KikoEmbeddingAutocompleteAPI.get_suggestions(
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prefix=prefix,
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max_results=max_results,
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include_embeddings=include_embeddings,
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include_loras=include_loras,
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case_sensitive=case_sensitive
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)
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return web.json_response(suggestions)
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@PromptServer.instance.routes.get("/kikotools/autocomplete/loras")
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async def get_loras(request):
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"""API endpoint for getting list of LoRAs."""
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print("[KikoTools] LoRA endpoint called")
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try:
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lora_files = folder_paths.get_filename_list("loras")
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print(f"[KikoTools] Found {len(lora_files)} LoRA files")
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# Return just the names without extensions
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loras = [os.path.splitext(f)[0] for f in lora_files]
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print(f"[KikoTools] Returning LoRAs: {loras[:5]}...") # Show first 5
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return web.json_response(loras)
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except Exception as e:
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print(f"[KikoTools] Error getting LoRAs: {e}")
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return web.json_response([])
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print("[KikoTools] Autocomplete API endpoints registered successfully")
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except ImportError as e:
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print(f"[KikoTools] Could not setup autocomplete API: {e}")
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# Import server components at module level to ensure they're available
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try:
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from aiohttp import web
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from server import PromptServer
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import folder_paths
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# Setup API if available
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setup_autocomplete_api()
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print("[KikoTools] Server imports successful")
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# Register autocomplete endpoints directly
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@PromptServer.instance.routes.get("/kikotools/autocomplete/embeddings")
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async def get_embeddings(request):
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"""API endpoint for getting list of embeddings with full paths."""
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print("[KikoTools] Embeddings endpoint called")
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try:
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embedding_files = folder_paths.get_filename_list("embeddings")
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print(f"[KikoTools] Found {len(embedding_files)} embedding files")
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# Return embeddings with their subdirectory paths, without extensions
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embeddings = []
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for f in embedding_files:
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# Remove extension but keep subdirectory path
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clean_path = os.path.splitext(f)[0]
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embeddings.append(
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{
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"file_name": clean_path,
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"model_name": clean_path,
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"name": os.path.basename(clean_path),
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"path": clean_path,
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}
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)
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if len(embeddings) > 0:
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print(f"[KikoTools] Sample embedding: {embeddings[0]}")
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print(f"[KikoTools] Returning {len(embeddings)} embeddings with paths")
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return web.json_response(embeddings)
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except Exception as e:
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print(f"[KikoTools] Error getting embeddings: {e}")
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import traceback
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traceback.print_exc()
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return web.json_response([])
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@PromptServer.instance.routes.get("/kikotools/autocomplete/loras")
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async def get_loras(request):
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"""API endpoint for getting list of LoRAs."""
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print("[KikoTools] LoRA endpoint called")
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try:
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lora_files = folder_paths.get_filename_list("loras")
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print(f"[KikoTools] Found {len(lora_files)} LoRA files")
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# Return LoRAs with paths
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loras = []
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for f in lora_files:
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clean_path = os.path.splitext(f)[0]
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loras.append(
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{
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"name": os.path.basename(clean_path),
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"path": clean_path,
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"file": f,
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}
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)
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print(f"[KikoTools] Returning {len(loras)} LoRAs")
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return web.json_response(loras)
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except Exception as e:
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print(f"[KikoTools] Error getting LoRAs: {e}")
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import traceback
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traceback.print_exc()
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return web.json_response([])
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print("[KikoTools] Autocomplete API endpoints registered successfully")
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print(
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"[KikoTools] Routes available: /kikotools/autocomplete/embeddings and /kikotools/autocomplete/loras"
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)
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except ImportError as e:
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print(f"[KikoTools] Could not import server components: {e}")
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except Exception as e:
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print(f"[KikoTools] Unexpected error setting up API: {e}")
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import traceback
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traceback.print_exc()
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# API endpoints are registered above at module import time
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def get_version():
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Binary file not shown.
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After Width: | Height: | Size: 40 KiB |
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After Width: | Height: | Size: 41 KiB |
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After Width: | Height: | Size: 34 KiB |
@@ -16,10 +16,14 @@ from .tools.resolution_calculator import ResolutionCalculatorNode
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from .tools.sampler_combo import SamplerComboCompactNode, SamplerComboNode
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from .tools.seed_history import SeedHistoryNode
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from .tools.width_height_selector import WidthHeightSelectorNode
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from .tools.xyz_helpers import (FluxSamplerParamsNode, LoRAFolderBatchNode,
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PlotParametersNode, SamplerSelectHelperNode,
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SchedulerSelectHelperNode,
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TextEncodeSamplerParamsNode)
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from .tools.xyz_helpers import (
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FluxSamplerParamsNode,
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LoRAFolderBatchNode,
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PlotParametersNode,
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SamplerSelectHelperNode,
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SchedulerSelectHelperNode,
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TextEncodeSamplerParamsNode,
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)
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# ComfyUI node registration mappings
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NODE_CLASS_MAPPINGS = {
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@@ -65,7 +69,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"FluxSamplerParams": "Flux Sampler Parameters",
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"PlotParameters+": "Plot Parameters",
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"LoRAFolderBatch": "LoRA Folder Batch",
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"KikoEmbeddingAutocomplete": "🫶 Embedding Autocomplete (Test Panel)",
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"KikoEmbeddingAutocomplete": "🫶 Embedding Autocomplete Configuration",
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}
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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@@ -4,15 +4,14 @@ Provides autocomplete functionality for embeddings and LoRAs in text inputs.
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"""
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import os
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import json
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from typing import Dict, List, Any, Optional
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from typing import Dict, List, Any
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import folder_paths
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class KikoEmbeddingAutocomplete:
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"""Node that provides embedding autocomplete functionality."""
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DISPLAY_NAME = "🫶 Embedding Autocomplete (Test Panel)"
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DISPLAY_NAME = "🫶 Embedding Autocomplete Settings"
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CATEGORY = "ComfyAssets"
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# Settings definition for the settings registry
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@@ -20,72 +19,67 @@ class KikoEmbeddingAutocomplete:
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"enabled": {
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"type": "boolean",
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"default": True,
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"description": "Enable embedding autocomplete",
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},
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"trigger_chars": {
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"type": "combo",
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"default": 2,
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"options": [1, 2, 3, 4],
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"description": "Number of characters before showing suggestions",
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},
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"max_suggestions": {
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"type": "combo",
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"default": 20,
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"options": [10, 20, 30, 50],
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"description": "Maximum number of suggestions to show",
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"description": "Enable autocomplete",
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},
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"show_embeddings": {
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"type": "boolean",
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"default": True,
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"description": "Show embeddings in suggestions",
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"description": "Show embeddings in autocomplete",
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},
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"show_loras": {
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"type": "boolean",
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"default": True,
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"description": "Show LoRAs in suggestions",
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"description": "Show LoRAs in autocomplete",
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},
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"case_sensitive": {
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"embedding_trigger": {
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"type": "text",
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"default": "embedding:",
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"description": "Trigger text for embeddings (e.g., 'embedding:', 'emb:', or custom)",
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},
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"lora_trigger": {
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"type": "text",
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"default": "<lora:",
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"description": "Trigger text for LoRAs (e.g., '<lora:', 'lora:', or custom)",
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},
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"quick_trigger": {
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"type": "text",
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"default": "em",
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"description": "Quick trigger to show embeddings (e.g., 'em', 'emb', or disabled with '')",
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},
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"min_chars": {
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"type": "combo",
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"default": 2,
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"options": [1, 2, 3, 4, 5],
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"description": "Minimum characters before showing suggestions",
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},
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"max_suggestions": {
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"type": "combo",
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"default": 20,
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"options": [5, 10, 15, 20, 30, 50, 100],
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"description": "Maximum number of suggestions to display",
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},
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"sort_by_directory": {
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"type": "boolean",
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"default": False,
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"description": "Case sensitive matching",
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"default": True,
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"description": "Group suggestions by directory",
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},
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}
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@classmethod
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def INPUT_TYPES(cls):
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"""Define input types for the node."""
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hints = """🫶 Embedding Autocomplete Test Panel
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Try these triggers in the text field below:
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• Type 'embedding:' to list all embeddings
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• Type '<lora:' to list all LoRAs
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• Start typing any name to filter results
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• Use Tab or Enter to select, Escape to cancel
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• Arrow keys to navigate suggestions
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This autocomplete works in all prompt fields!"""
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return {
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"required": {
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"test_input": ("STRING", {
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"multiline": True,
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"default": hints,
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"placeholder": "Type here to test autocomplete..."
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}),
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},
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"optional": {
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"refresh": ("BOOLEAN", {"default": False, "label_on": "Refresh Lists", "label_off": "Use Cache"}),
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},
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"required": {},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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}
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},
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}
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RETURN_TYPES = ("STRING", "INT", "INT")
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RETURN_NAMES = ("test_output", "embeddings_count", "loras_count")
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FUNCTION = "process_autocomplete"
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RETURN_TYPES = ()
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RETURN_NAMES = ()
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FUNCTION = "update_settings"
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OUTPUT_NODE = True
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@classmethod
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def VALIDATE_INPUTS(cls, **kwargs):
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return True
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@@ -94,61 +88,24 @@ This autocomplete works in all prompt fields!"""
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self.embeddings_cache = None
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self.loras_cache = None
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def process_autocomplete(self, test_input, refresh=False, unique_id=None):
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"""Process and display autocomplete information.
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def update_settings(self, unique_id=None):
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"""Update settings display.
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This node serves as both a test panel and information display
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for the embedding autocomplete functionality.
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This node serves as a settings indicator.
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Actual settings are configured in ComfyUI Settings menu.
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"""
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if refresh or self.embeddings_cache is None:
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self.refresh_cache()
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# Count available resources
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embeddings_count = len(self.embeddings_cache)
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loras_count = len(self.loras_cache)
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# Generate informative output
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output_lines = [
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f"🫶 Embedding Autocomplete Status",
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f"═" * 40,
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f"📦 Embeddings found: {embeddings_count}",
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f"🎨 LoRAs found: {loras_count}",
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f"",
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f"✅ Autocomplete is {'enabled' if self.embeddings_cache or self.loras_cache else 'ready (no resources found)'}",
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f"",
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f"Your test input:",
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f"─" * 40,
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test_input,
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f"─" * 40,
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f"",
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f"💡 Tips:",
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f"• This node enables autocomplete in ALL prompt fields",
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f"• Settings available in ComfyUI Settings menu",
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f"• Look for 🫶 Embedding Autocomplete options"
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]
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if embeddings_count > 0:
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output_lines.append(f"")
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output_lines.append(f"Sample embeddings (first 5):")
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for emb in self.embeddings_cache[:5]:
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output_lines.append(f" • {emb['name']}")
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if loras_count > 0:
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output_lines.append(f"")
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output_lines.append(f"Sample LoRAs (first 5):")
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for lora in self.loras_cache[:5]:
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output_lines.append(f" • {lora['name']}")
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output_text = "\n".join(output_lines)
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return (output_text, embeddings_count, loras_count)
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# This node doesn't actually process anything
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||||
# It's just a visual indicator that autocomplete is available
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return ()
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||||
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||||
def refresh_cache(self):
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"""Refresh the cache of embeddings and LoRAs."""
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print("[KikoEmbeddingAutocomplete] Refreshing cache...")
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self.embeddings_cache = self.get_embeddings()
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self.loras_cache = self.get_loras()
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print(f"[KikoEmbeddingAutocomplete] Cached {len(self.embeddings_cache)} embeddings, {len(self.loras_cache)} LoRAs")
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||||
print(
|
||||
f"[KikoEmbeddingAutocomplete] Cached {len(self.embeddings_cache)} embeddings, {len(self.loras_cache)} LoRAs"
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||||
)
|
||||
|
||||
def get_embeddings(self) -> List[Dict[str, Any]]:
|
||||
"""Get list of available embeddings."""
|
||||
@@ -158,7 +115,9 @@ This autocomplete works in all prompt fields!"""
|
||||
try:
|
||||
print("[KikoEmbeddingAutocomplete] Getting embeddings list...")
|
||||
embedding_files = folder_paths.get_filename_list("embeddings")
|
||||
print(f"[KikoEmbeddingAutocomplete] Found {len(embedding_files)} embedding files")
|
||||
print(
|
||||
f"[KikoEmbeddingAutocomplete] Found {len(embedding_files)} embedding files"
|
||||
)
|
||||
for file in embedding_files:
|
||||
name = os.path.splitext(file)[0]
|
||||
embeddings.append(
|
||||
@@ -212,7 +171,7 @@ This autocomplete works in all prompt fields!"""
|
||||
|
||||
# Return combined modification time
|
||||
return os.path.getmtime(embeddings_path) + os.path.getmtime(loras_path)
|
||||
except:
|
||||
except Exception:
|
||||
return 0
|
||||
|
||||
|
||||
|
||||
@@ -3,10 +3,17 @@ pytest configuration and fixtures for ComfyUI-KikoTools testing
|
||||
Provides mock ComfyUI environments and test data
|
||||
"""
|
||||
|
||||
import sys
|
||||
import pytest
|
||||
import torch
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
# Mock folder_paths module before any imports that might use it
|
||||
sys.modules["folder_paths"] = MagicMock()
|
||||
sys.modules["folder_paths"].get_filename_list = MagicMock(return_value=[])
|
||||
sys.modules["folder_paths"].get_folder_paths = MagicMock(return_value=["/mock/path"])
|
||||
sys.modules["folder_paths"].base_path = "/mock/base"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_image_tensor():
|
||||
|
||||
@@ -4,77 +4,84 @@ import sys
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
# Mock ComfyUI's folder_paths module
|
||||
sys.modules['folder_paths'] = MagicMock()
|
||||
sys.modules['folder_paths'].get_filename_list = MagicMock(return_value=[])
|
||||
sys.modules['folder_paths'].get_folder_paths = MagicMock(return_value=["/mock/path"])
|
||||
sys.modules['folder_paths'].base_path = "/mock/base"
|
||||
sys.modules["folder_paths"] = MagicMock()
|
||||
sys.modules["folder_paths"].get_filename_list = MagicMock(return_value=[])
|
||||
sys.modules["folder_paths"].get_folder_paths = MagicMock(return_value=["/mock/path"])
|
||||
sys.modules["folder_paths"].base_path = "/mock/base"
|
||||
|
||||
|
||||
def test_import():
|
||||
"""Test that the module can be imported."""
|
||||
from kikotools.tools.embedding_autocomplete import KikoEmbeddingAutocomplete
|
||||
|
||||
assert KikoEmbeddingAutocomplete is not None
|
||||
assert KikoEmbeddingAutocomplete.DISPLAY_NAME == "🫶 Embedding Autocomplete (Test Panel)"
|
||||
assert (
|
||||
KikoEmbeddingAutocomplete.DISPLAY_NAME
|
||||
== "🫶 Embedding Autocomplete Settings"
|
||||
)
|
||||
assert KikoEmbeddingAutocomplete.CATEGORY == "ComfyAssets"
|
||||
|
||||
|
||||
def test_settings_defined():
|
||||
"""Test that settings are properly defined."""
|
||||
from kikotools.tools.embedding_autocomplete import KikoEmbeddingAutocomplete
|
||||
|
||||
|
||||
settings = KikoEmbeddingAutocomplete.SETTINGS
|
||||
assert "enabled" in settings
|
||||
assert "trigger_chars" in settings
|
||||
assert "min_chars" in settings # Changed from trigger_chars
|
||||
assert "max_suggestions" in settings
|
||||
assert "show_embeddings" in settings
|
||||
assert "show_loras" in settings
|
||||
assert "case_sensitive" in settings
|
||||
|
||||
assert "embedding_trigger" in settings
|
||||
assert "lora_trigger" in settings
|
||||
assert "quick_trigger" in settings
|
||||
assert "sort_by_directory" in settings
|
||||
|
||||
# Check settings structure
|
||||
assert settings["enabled"]["type"] == "boolean"
|
||||
assert settings["enabled"]["default"] is True
|
||||
assert settings["trigger_chars"]["type"] == "combo"
|
||||
assert settings["trigger_chars"]["options"] == [1, 2, 3, 4]
|
||||
assert settings["min_chars"]["type"] == "combo"
|
||||
assert settings["min_chars"]["options"] == [1, 2, 3, 4, 5]
|
||||
|
||||
|
||||
def test_input_types():
|
||||
"""Test INPUT_TYPES class method."""
|
||||
from kikotools.tools.embedding_autocomplete import KikoEmbeddingAutocomplete
|
||||
|
||||
|
||||
input_types = KikoEmbeddingAutocomplete.INPUT_TYPES()
|
||||
assert "required" in input_types
|
||||
assert "optional" in input_types
|
||||
assert "test_input" in input_types["required"]
|
||||
assert "refresh" in input_types["optional"]
|
||||
assert "hidden" in input_types
|
||||
assert input_types["required"] == {} # No required inputs
|
||||
assert "unique_id" in input_types["hidden"]
|
||||
|
||||
|
||||
def test_api_suggestions():
|
||||
"""Test the API suggestions method."""
|
||||
from kikotools.tools.embedding_autocomplete.node import KikoEmbeddingAutocompleteAPI
|
||||
|
||||
|
||||
# Mock folder_paths to return some test files
|
||||
sys.modules['folder_paths'].get_filename_list = MagicMock(
|
||||
side_effect=lambda x: ["test1.pt", "test2.safetensors"] if x == "embeddings" else ["lora1.pt", "lora2.safetensors"]
|
||||
sys.modules["folder_paths"].get_filename_list = MagicMock(
|
||||
side_effect=lambda x: (
|
||||
["test1.pt", "test2.safetensors"]
|
||||
if x == "embeddings"
|
||||
else ["lora1.pt", "lora2.safetensors"]
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
# Test with embeddings
|
||||
suggestions = KikoEmbeddingAutocompleteAPI.get_suggestions(
|
||||
prefix="test",
|
||||
include_embeddings=True,
|
||||
include_loras=False
|
||||
prefix="test", include_embeddings=True, include_loras=False
|
||||
)
|
||||
|
||||
|
||||
assert len(suggestions) == 2
|
||||
assert suggestions[0]["type"] == "embedding"
|
||||
assert suggestions[0]["name"] == "test1"
|
||||
|
||||
|
||||
# Test with LoRAs
|
||||
suggestions = KikoEmbeddingAutocompleteAPI.get_suggestions(
|
||||
prefix="lora",
|
||||
include_embeddings=False,
|
||||
include_loras=True
|
||||
prefix="lora", include_embeddings=False, include_loras=True
|
||||
)
|
||||
|
||||
|
||||
assert len(suggestions) == 2
|
||||
assert suggestions[0]["type"] == "lora"
|
||||
assert "<lora:" in suggestions[0]["value"]
|
||||
@@ -85,4 +92,4 @@ if __name__ == "__main__":
|
||||
test_settings_defined()
|
||||
test_input_types()
|
||||
test_api_suggestions()
|
||||
print("All tests passed!")
|
||||
print("All tests passed!")
|
||||
|
||||
@@ -0,0 +1,38 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Test script to check how ComfyUI returns embedding paths."""
|
||||
|
||||
import sys
|
||||
import os
|
||||
|
||||
# Add ComfyUI to path if available
|
||||
comfyui_path = os.path.expanduser("~/ComfyUI")
|
||||
if os.path.exists(comfyui_path):
|
||||
sys.path.insert(0, comfyui_path)
|
||||
|
||||
try:
|
||||
import folder_paths
|
||||
|
||||
print("Testing embedding paths...")
|
||||
print("=" * 50)
|
||||
|
||||
# Get embeddings
|
||||
embeddings = folder_paths.get_filename_list("embeddings")
|
||||
print(f"Total embeddings found: {len(embeddings)}")
|
||||
print("\nFirst 20 embeddings:")
|
||||
for i, emb in enumerate(embeddings[:20]):
|
||||
print(f" {i+1}. '{emb}'")
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("Checking for path separators...")
|
||||
has_paths = any("/" in emb or "\\" in emb for emb in embeddings)
|
||||
print(f"Contains path separators: {has_paths}")
|
||||
|
||||
if has_paths:
|
||||
print("\nEmbeddings with paths:")
|
||||
for emb in embeddings[:10]:
|
||||
if "/" in emb or "\\" in emb:
|
||||
print(f" - {emb}")
|
||||
|
||||
except ImportError as e:
|
||||
print(f"Could not import folder_paths: {e}")
|
||||
print("\nThis script should be run from within ComfyUI environment")
|
||||
@@ -0,0 +1,60 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Test what folder_paths.get_filename_list actually returns."""
|
||||
|
||||
import sys
|
||||
import os
|
||||
|
||||
# Add ComfyUI to path
|
||||
comfyui_path = "/home/vito/ai-apps/ComfyUI-3.12"
|
||||
if os.path.exists(comfyui_path):
|
||||
sys.path.insert(0, comfyui_path)
|
||||
# Set the working directory for folder_paths
|
||||
os.environ["COMFYUI_PATH"] = comfyui_path
|
||||
|
||||
try:
|
||||
import folder_paths
|
||||
|
||||
print("Testing folder_paths.get_filename_list('embeddings')...")
|
||||
print("=" * 60)
|
||||
|
||||
embeddings = folder_paths.get_filename_list("embeddings")
|
||||
print(f"Total embeddings: {len(embeddings)}")
|
||||
|
||||
print("\nFirst 10 embeddings:")
|
||||
for i, emb in enumerate(embeddings[:10]):
|
||||
print(f" {i+1}. '{emb}'")
|
||||
|
||||
# Check if any have paths
|
||||
with_paths = [e for e in embeddings if "/" in e or "\\" in e]
|
||||
print(f"\nEmbeddings with path separators: {len(with_paths)}")
|
||||
if with_paths:
|
||||
print("Examples:")
|
||||
for e in with_paths[:5]:
|
||||
print(f" - '{e}'")
|
||||
|
||||
# Check the actual folder structure
|
||||
print("\n" + "=" * 60)
|
||||
print("Checking actual folder structure...")
|
||||
emb_folders = folder_paths.get_folder_paths("embeddings")
|
||||
print(f"Embedding folders: {emb_folders}")
|
||||
|
||||
if emb_folders:
|
||||
emb_dir = emb_folders[0]
|
||||
print(f"\nContents of {emb_dir}:")
|
||||
for root, dirs, files in os.walk(emb_dir):
|
||||
rel_root = os.path.relpath(root, emb_dir)
|
||||
if rel_root == ".":
|
||||
rel_root = ""
|
||||
for f in files[:5]: # Show first 5 files in each dir
|
||||
if f.endswith((".pt", ".safetensors", ".ckpt")):
|
||||
full_path = os.path.join(rel_root, f) if rel_root else f
|
||||
print(f" - '{full_path}'")
|
||||
if len(files) > 5:
|
||||
print(f" ... and {len(files)-5} more files")
|
||||
if dirs:
|
||||
print(f" Subdirectories: {dirs}")
|
||||
|
||||
except ImportError as e:
|
||||
print(f"Could not import folder_paths: {e}")
|
||||
else:
|
||||
print(f"ComfyUI not found at {comfyui_path}")
|
||||
+1080
-245
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user